Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.


Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Write your Algorithm
  • Step 6: Test Your Algorithm

Step 0: Import Datasets

Make sure that you've downloaded the required human and dog datasets:

Note: if you are using the Udacity workspace, you DO NOT need to re-download these - they can be found in the /data folder as noted in the cell below.

  • Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location /dog_images.

  • Download the human dataset. Unzip the folder and place it in the home directory, at location /lfw.

Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.

In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays human_files and dog_files.

In [1]:
import numpy as np
from glob import glob

# load filenames for human and dog images
human_files = np.array(glob("/data/lfw/*/*"))
dog_files = np.array(glob("/data/dog_images/*/*/*"))

# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
There are 13233 total human images.
There are 8351 total dog images.

Step 1: Detect Humans

In this section, we use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.

OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory. In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [2]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[0])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [3]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

  • Percentage of human images with a detected human face is: 98
  • Percentage of dog images with a detected human face is: 17
In [4]:
from tqdm import tqdm

human_files_short = human_files[:100]
dog_files_short = dog_files[:100]

#-#-# Do NOT modify the code above this line. #-#-#

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
detected_human = 0
detected_dog = 0
for i in range(len(human_files_short)):
    if face_detector(human_files_short[i]):
        detected_human +=1
    if face_detector(dog_files_short[i]):
        detected_dog +=1

print("Percentage of human images with a detected human face is:", detected_human)
print("Percentage of dog images with a detected human face is:", detected_dog)
Percentage of human images with a detected human face is: 98
Percentage of dog images with a detected human face is: 17

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.

In [5]:
### (Optional) 
### TODO: Test performance of anotherface detection algorithm.
### Feel free to use as many code cells as needed.
def face_detector_show(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    # convert BGR image to RGB for plotting
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    # display the image, along with bounding box
    for (x,y,w,h) in faces:
        # add bounding box to color image
        cv2.rectangle(cv_rgb,(x,y),(x+w,y+h),(255,0,0),2)
    return len(faces) > 0, cv_rgb
In [6]:
# Lets check dog images with a detected human face
for i in range(len(human_files_short)):
    detected, img = face_detector_show(dog_files_short[i])
    if detected:
        plt.imshow(img)
        plt.show()
In [7]:
# Lets check human images which are not detected as human face
for i in range(len(human_files_short)):
    detected, img = face_detector_show(human_files_short[i])
    if not detected:
        plt.imshow(img)
        plt.show()

Step 2: Detect Dogs

In this section, we use a pre-trained model to detect dogs in images.

Obtain Pre-trained VGG-16 Model

The code cell below downloads the VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.

In [8]:
import torch
import torchvision.models as models

# define VGG16 model
VGG16 = models.vgg16(pretrained=True)

# check if CUDA is available
use_cuda = torch.cuda.is_available()

# move model to GPU if CUDA is available
if use_cuda:
    VGG16 = VGG16.cuda()
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.torch/models/vgg16-397923af.pth
100%|██████████| 553433881/553433881 [00:05<00:00, 94189299.16it/s] 
In [9]:
VGG16
Out[9]:
VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace)
    (2): Dropout(p=0.5)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace)
    (5): Dropout(p=0.5)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)

Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.

(IMPLEMENTATION) Making Predictions with a Pre-trained Model

In the next code cell, you will write a function that accepts a path to an image (such as 'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg') as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model. The output should always be an integer between 0 and 999, inclusive.

Before writing the function, make sure that you take the time to learn how to appropriately pre-process tensors for pre-trained models in the PyTorch documentation.

In [10]:
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import torchvision.transforms as transforms

def VGG16_predict(img_path):
    '''
    Use pre-trained VGG-16 model to obtain index corresponding to 
    predicted ImageNet class for image at specified path
    
    Args:
        img_path: path to an image
        
    Returns:
        Index corresponding to VGG-16 model's prediction
    '''
    
    ## TODO: Complete the function.
    ## Load and pre-process an image from the given img_path
    ## Return the *index* of the predicted class for that image
    image = Image.open(img_path).convert('RGB')
    
    in_transform = transforms.Compose([
                    transforms.Resize((224, 224)),
                    transforms.ToTensor(),
                    transforms.Normalize((0.5, 0.5, 0.5), 
                                         (0.5, 0.5, 0.5))])
    
    img_processed = in_transform(image).unsqueeze(0)
    
    if use_cuda:
        img_processed = img_processed.to('cuda')
        
    VGG16.eval()
    output = VGG16(img_processed)
    _, top_class = torch.max(output, 1)
    
    return top_class # predicted class index

print(VGG16_predict(dog_files_short[0]))

    
tensor([ 243], device='cuda:0')

(IMPLEMENTATION) Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).

Use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [11]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    ## TODO: Complete the function.
    returned_class = VGG16_predict(img_path)
    if 151 <= returned_class <= 268:
        return True
    else:
        return False     # true/false

(IMPLEMENTATION) Assess the Dog Detector

Question 2: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

  • Percentage of dog images from human files is: 0
  • Percentage of dog images from dog files is: 100
In [12]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
detected_dog_from_human_files = 0
detected_dog_from_dog_files = 0
for i in range(len(human_files_short)):
    if dog_detector(human_files_short[i]):
        detected_dog_from_human_files +=1
    if dog_detector(dog_files_short[i]):
        detected_dog_from_dog_files +=1

print("Percentage of dog images from human files is:", detected_dog_from_human_files)
print("Percentage of dog images from dog files is:", detected_dog_from_dog_files)
Percentage of dog images from human files is: 0
Percentage of dog images from dog files is: 100

We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as Inception-v3, ResNet-50, etc). Please use the code cell below to test other pre-trained PyTorch models. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.


Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dog_images/train, dog_images/valid, and dog_images/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!

In [13]:
!ls /data/dog_images
test  train  valid
In [14]:
!pwd
/home/workspace/dog_project
In [15]:
import os
from torchvision import datasets

### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes
dir_test = os.path.join("/data/dog_images", "test")
dir_valid = os.path.join("/data/dog_images", "valid")
dir_train = os.path.join("/data/dog_images", "train")
train_transforms = transforms.Compose([transforms.RandomRotation(30),
                                       transforms.Resize(225),
                                       transforms.CenterCrop(224),
                                       transforms.RandomHorizontalFlip(),
                                       transforms.ToTensor(),
                                       transforms.Normalize((0.5,),(0.5,))])

valid_transforms = transforms.Compose([transforms.Resize(255),
                                       transforms.CenterCrop(224),
                                       transforms.ToTensor(),
                                       transforms.Normalize((0.5,),(0.5,))])
train_data = datasets.ImageFolder(dir_train, transform=train_transforms)
valid_data = datasets.ImageFolder(dir_valid, transform=valid_transforms)
test_data = datasets.ImageFolder(dir_test, transform=valid_transforms)

train_loader = torch.utils.data.DataLoader(train_data, batch_size=40, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=40)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=40)

loaders_scratch = {
    'train': train_loader,
    'test': test_loader,
    'valid': valid_loader
}
In [16]:
train_data.classes
Out[16]:
['001.Affenpinscher',
 '002.Afghan_hound',
 '003.Airedale_terrier',
 '004.Akita',
 '005.Alaskan_malamute',
 '006.American_eskimo_dog',
 '007.American_foxhound',
 '008.American_staffordshire_terrier',
 '009.American_water_spaniel',
 '010.Anatolian_shepherd_dog',
 '011.Australian_cattle_dog',
 '012.Australian_shepherd',
 '013.Australian_terrier',
 '014.Basenji',
 '015.Basset_hound',
 '016.Beagle',
 '017.Bearded_collie',
 '018.Beauceron',
 '019.Bedlington_terrier',
 '020.Belgian_malinois',
 '021.Belgian_sheepdog',
 '022.Belgian_tervuren',
 '023.Bernese_mountain_dog',
 '024.Bichon_frise',
 '025.Black_and_tan_coonhound',
 '026.Black_russian_terrier',
 '027.Bloodhound',
 '028.Bluetick_coonhound',
 '029.Border_collie',
 '030.Border_terrier',
 '031.Borzoi',
 '032.Boston_terrier',
 '033.Bouvier_des_flandres',
 '034.Boxer',
 '035.Boykin_spaniel',
 '036.Briard',
 '037.Brittany',
 '038.Brussels_griffon',
 '039.Bull_terrier',
 '040.Bulldog',
 '041.Bullmastiff',
 '042.Cairn_terrier',
 '043.Canaan_dog',
 '044.Cane_corso',
 '045.Cardigan_welsh_corgi',
 '046.Cavalier_king_charles_spaniel',
 '047.Chesapeake_bay_retriever',
 '048.Chihuahua',
 '049.Chinese_crested',
 '050.Chinese_shar-pei',
 '051.Chow_chow',
 '052.Clumber_spaniel',
 '053.Cocker_spaniel',
 '054.Collie',
 '055.Curly-coated_retriever',
 '056.Dachshund',
 '057.Dalmatian',
 '058.Dandie_dinmont_terrier',
 '059.Doberman_pinscher',
 '060.Dogue_de_bordeaux',
 '061.English_cocker_spaniel',
 '062.English_setter',
 '063.English_springer_spaniel',
 '064.English_toy_spaniel',
 '065.Entlebucher_mountain_dog',
 '066.Field_spaniel',
 '067.Finnish_spitz',
 '068.Flat-coated_retriever',
 '069.French_bulldog',
 '070.German_pinscher',
 '071.German_shepherd_dog',
 '072.German_shorthaired_pointer',
 '073.German_wirehaired_pointer',
 '074.Giant_schnauzer',
 '075.Glen_of_imaal_terrier',
 '076.Golden_retriever',
 '077.Gordon_setter',
 '078.Great_dane',
 '079.Great_pyrenees',
 '080.Greater_swiss_mountain_dog',
 '081.Greyhound',
 '082.Havanese',
 '083.Ibizan_hound',
 '084.Icelandic_sheepdog',
 '085.Irish_red_and_white_setter',
 '086.Irish_setter',
 '087.Irish_terrier',
 '088.Irish_water_spaniel',
 '089.Irish_wolfhound',
 '090.Italian_greyhound',
 '091.Japanese_chin',
 '092.Keeshond',
 '093.Kerry_blue_terrier',
 '094.Komondor',
 '095.Kuvasz',
 '096.Labrador_retriever',
 '097.Lakeland_terrier',
 '098.Leonberger',
 '099.Lhasa_apso',
 '100.Lowchen',
 '101.Maltese',
 '102.Manchester_terrier',
 '103.Mastiff',
 '104.Miniature_schnauzer',
 '105.Neapolitan_mastiff',
 '106.Newfoundland',
 '107.Norfolk_terrier',
 '108.Norwegian_buhund',
 '109.Norwegian_elkhound',
 '110.Norwegian_lundehund',
 '111.Norwich_terrier',
 '112.Nova_scotia_duck_tolling_retriever',
 '113.Old_english_sheepdog',
 '114.Otterhound',
 '115.Papillon',
 '116.Parson_russell_terrier',
 '117.Pekingese',
 '118.Pembroke_welsh_corgi',
 '119.Petit_basset_griffon_vendeen',
 '120.Pharaoh_hound',
 '121.Plott',
 '122.Pointer',
 '123.Pomeranian',
 '124.Poodle',
 '125.Portuguese_water_dog',
 '126.Saint_bernard',
 '127.Silky_terrier',
 '128.Smooth_fox_terrier',
 '129.Tibetan_mastiff',
 '130.Welsh_springer_spaniel',
 '131.Wirehaired_pointing_griffon',
 '132.Xoloitzcuintli',
 '133.Yorkshire_terrier']

Question 3: Describe your chosen procedure for preprocessing the data.

  • How does your code resize the images (by cropping, stretching, etc)? What size did you pick for the input tensor, and why?
  • Did you decide to augment the dataset? If so, how (through translations, flips, rotations, etc)? If not, why not?

Answer:First a resize of 225x225 is applied to normalize the training set image, where most of the images will be shrinked, and some few stretched. After a random crop is applied with a size of 224x224. A size of 224 was chosen, similar to what VGG authors use, to try to keep the most information detail possible in the images, while going through multiple pooling layers to reduce its size.

Random flip and rotation is applied to enhance the training process. No translation was applied to avoid decentering the pictures from dog faces

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. Use the template in the code cell below.

In [17]:
import torch.nn as nn
import torch.nn.functional as F

# define the CNN architecture
class Net(nn.Module):
    ### TODO: choose an architecture, and complete the class
    def __init__(self):
        super(Net, self).__init__()
        ## Define layers of a CNN
        hidden_1 = 1024
        hidden_2 = 512
        hidden_3 = 64


        self.conv11 = nn.Conv2d(3, 16, 3, padding=1)
        self.conv12 = nn.Conv2d(16, 16, 3, padding=1)
        self.conv13 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
        self.maxpool = nn.MaxPool2d(2,2)

        self.bn_1 = nn.BatchNorm2d(32)
        self.bn_2 = nn.BatchNorm2d(32)
        self.bn_3 = nn.BatchNorm2d(64)
        self.bn_4 = nn.BatchNorm2d(128)
        self.bn_5 = nn.BatchNorm2d(256)

        self.fc_1 = nn.Linear(256*7*7, hidden_1)
        self.fc_2 = nn.Linear(hidden_1, hidden_2)
        self.fc_3 = nn.Linear(hidden_2, hidden_3)
        self.fc_4 = nn.Linear(hidden_3, 133)

        self.dropout12 = nn.Dropout2d(p = 0.5)
        self.bn1_1 = nn.BatchNorm1d(hidden_1)
        self.bn1_2 = nn.BatchNorm1d(hidden_2)
        self.bn1_3 = nn.BatchNorm1d(hidden_3)
        
    def forward(self, x):
        ## Define forward behavior
        x = F.relu(self.conv11(x))
        x = F.relu(self.conv12(x))
        x = self.bn_1(self.maxpool(F.relu(self.conv13(x))))
        x = self.dropout12(self.bn_2(self.maxpool(F.relu(self.conv2(x)))))
        x = self.bn_3(self.maxpool(F.relu(self.conv3(x))))
        x = self.bn_4(self.maxpool(F.relu(self.conv4(x))))
        x = self.dropout12(self.bn_5(self.maxpool(F.relu(self.conv5(x)))))

        x = x.view(-1, 256*7*7)
        x = self.bn1_1(F.relu(self.fc_1(x)))
        x = self.bn1_2(F.relu(self.fc_2(x)))
        x = self.bn1_3(F.relu(self.fc_3(x)))
        x = F.log_softmax(self.fc_4(x), dim=1)
        return x
    
    def forward(self, x):
        ## Define forward behavior
        x = F.relu(self.conv11(x))
        x = F.relu(self.conv12(x))
        x = self.bn_1(self.maxpool(F.relu(self.conv13(x))))
        x = self.dropout12(self.bn_2(self.maxpool(F.relu(self.conv2(x)))))
        x = self.bn_3(self.maxpool(F.relu(self.conv3(x))))
        x = self.bn_4(self.maxpool(F.relu(self.conv4(x))))
        x = self.dropout12(self.bn_5(self.maxpool(F.relu(self.conv5(x)))))
        
        x = x.view(-1, 256*7*7)
        x = self.bn1_1(F.relu(self.fc_1(x)))
        x = self.bn1_2(F.relu(self.fc_2(x)))
        x = self.bn1_3(F.relu(self.fc_3(x)))
        x = F.log_softmax(self.fc_4(x), dim=1)
        return x


#-#-# You so NOT have to modify the code below this line. #-#-#

# instantiate the CNN
model_scratch = Net()

# move tensors to GPU if CUDA is available
if use_cuda:
    model_scratch.cuda()
In [18]:
model_scratch
Out[18]:
Net(
  (conv11): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv12): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv13): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (bn_1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn_2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn_3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn_4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn_5): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (fc_1): Linear(in_features=12544, out_features=1024, bias=True)
  (fc_2): Linear(in_features=1024, out_features=512, bias=True)
  (fc_3): Linear(in_features=512, out_features=64, bias=True)
  (fc_4): Linear(in_features=64, out_features=133, bias=True)
  (dropout12): Dropout2d(p=0.5)
  (bn1_1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn1_2): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn1_3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.

Answer:

  • The first convolutional layer is defined to take 224 x 224 image having 3 input chanels as input and output 16 activation map and then Passed through a relu activation function. The second convolution layer is defined to take 16 activation maps as input and output 16 activation map and then Passed through a relu activation. For the first two convolution layers, Maxpooling layer was not used to retain the image spartial dimensions.
  • After that convoluttion layers are defined such a way that the dimension of the image is decreased and the activation map is increased and then convolution layers are passed through the batch normalisation to eliminate randomness and dropout is applied in some of the layers to prevent overfitting.
  • The output of last convolution layers are flattened and then passed throgh three fully connected layers and passed throgh relu activation function and batch normalisation.
  • The output of above mentioned fully connected layers are passed through fully connected layer having Log_softmax activation function for classification.

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and the optimizer as optimizer_scratch below.

In [19]:
import torch.optim as optim

### TODO: select loss function
criterion_scratch = nn.NLLLoss()

### TODO: select optimizer
optimizer_scratch = optim.Adam(model_scratch.parameters(), lr=0.001)

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_scratch.pt'.

In [20]:
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
    """returns trained model"""
    # initialize tracker for minimum validation loss
    valid_loss_min = np.Inf 
    
    for epoch in range(1, n_epochs+1):
        # initialize variables to monitor training and validation loss
        train_loss = 0.0
        valid_loss = 0.0
        
        ###################
        # train the model #
        ###################
        model.train()
        for batch_idx, (data, target) in enumerate(loaders['train']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            optimizer.zero_grad()
            logp = model.forward(data)
            loss = criterion(logp, target)
            loss.backward()
            optimizer.step() 
            ## find the loss and update the model parameters accordingly
            ## record the average training loss, using something like
            train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
            
        ######################    
        # validate the model #
        ######################
        model.eval()
        for batch_idx, (data, target) in enumerate(loaders['valid']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            logp = model.forward(data)
            loss = criterion(logp, target)
            valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data - valid_loss))
            ## update the average validation loss

            
        # print training/validation statistics 
        print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
            epoch, 
            train_loss,
            valid_loss
            ))
        
        ## TODO: save the model if validation loss has decreased
        if valid_loss <= valid_loss_min:
            torch.save(model.state_dict(), save_path)  
            valid_loss_min = valid_loss
            print('........initiating save mode')            
    # return trained model
    return model


# train the model
model_scratch = train(25, loaders_scratch, model_scratch, optimizer_scratch, 
                      criterion_scratch, use_cuda, 'model_scratch.pt')

# load the model that got the best validation accuracy
model_scratch.load_state_dict(torch.load('model_scratch.pt'))
Epoch: 1 	Training Loss: 4.807509 	Validation Loss: 4.687277
........initiating save mode
Epoch: 2 	Training Loss: 4.471549 	Validation Loss: 4.404094
........initiating save mode
Epoch: 3 	Training Loss: 4.311878 	Validation Loss: 4.426095
Epoch: 4 	Training Loss: 4.193388 	Validation Loss: 4.248815
........initiating save mode
Epoch: 5 	Training Loss: 4.075240 	Validation Loss: 4.127416
........initiating save mode
Epoch: 6 	Training Loss: 3.998126 	Validation Loss: 4.019804
........initiating save mode
Epoch: 7 	Training Loss: 3.889716 	Validation Loss: 4.318954
Epoch: 8 	Training Loss: 3.815179 	Validation Loss: 3.881942
........initiating save mode
Epoch: 9 	Training Loss: 3.740013 	Validation Loss: 3.858064
........initiating save mode
Epoch: 10 	Training Loss: 3.647617 	Validation Loss: 3.897732
Epoch: 11 	Training Loss: 3.564791 	Validation Loss: 3.701886
........initiating save mode
Epoch: 12 	Training Loss: 3.450893 	Validation Loss: 3.886198
Epoch: 13 	Training Loss: 3.383608 	Validation Loss: 3.572875
........initiating save mode
Epoch: 14 	Training Loss: 3.253147 	Validation Loss: 3.596771
Epoch: 15 	Training Loss: 3.168374 	Validation Loss: 3.429441
........initiating save mode
Epoch: 16 	Training Loss: 3.052519 	Validation Loss: 3.562726
Epoch: 17 	Training Loss: 2.943792 	Validation Loss: 3.418638
........initiating save mode
Epoch: 18 	Training Loss: 2.837054 	Validation Loss: 3.274237
........initiating save mode
Epoch: 19 	Training Loss: 2.744966 	Validation Loss: 3.198236
........initiating save mode
Epoch: 20 	Training Loss: 2.621045 	Validation Loss: 3.225266
Epoch: 21 	Training Loss: 2.522975 	Validation Loss: 3.151161
........initiating save mode
Epoch: 22 	Training Loss: 2.412591 	Validation Loss: 3.069539
........initiating save mode
Epoch: 23 	Training Loss: 2.310749 	Validation Loss: 2.995447
........initiating save mode
Epoch: 24 	Training Loss: 2.181733 	Validation Loss: 3.067174
Epoch: 25 	Training Loss: 2.071960 	Validation Loss: 3.049443

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 10%.

In [21]:
def test(loaders, model, criterion, use_cuda):

    # monitor test loss and accuracy
    test_loss = 0.
    correct = 0.
    total = 0.

    model.eval()
    for batch_idx, (data, target) in enumerate(loaders['test']):
        # move to GPU
        if use_cuda:
            data, target = data.cuda(), target.cuda()
        # forward pass: compute predicted outputs by passing inputs to the model
        output = model(data)
        # calculate the loss
        loss = criterion(output, target)
        # update average test loss 
        test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
        # convert output probabilities to predicted class
        pred = output.data.max(1, keepdim=True)[1]
        # compare predictions to true label
        correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
        total += data.size(0)
            
    print('Test Loss: {:.6f}\n'.format(test_loss))

    print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
        100. * correct / total, correct, total))

# call test function    
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)
Test Loss: 2.984463


Test Accuracy: 27% (231/836)

Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively).

If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.

In [22]:
## TODO: Specify data loaders

dir_test = os.path.join("/data/dog_images", "test")
dir_valid = os.path.join("/data/dog_images", "valid")
dir_train = os.path.join("/data/dog_images", "train")
train_transforms_dog = transforms.Compose([transforms.RandomRotation(30),
                                       transforms.Resize(225),
                                       transforms.CenterCrop(224),
                                       transforms.RandomHorizontalFlip(),
                                       transforms.ToTensor(),
                                       transforms.Normalize((0.5,),(0.5,))])

valid_transforms_dog = transforms.Compose([transforms.Resize(255),
                                       transforms.CenterCrop(224),
                                       transforms.ToTensor(),
                                       transforms.Normalize((0.5,),(0.5,))])
train_data_dog = datasets.ImageFolder(dir_train, transform=train_transforms_dog)
valid_data_dog = datasets.ImageFolder(dir_valid, transform=valid_transforms_dog)
test_data_dog = datasets.ImageFolder(dir_test, transform=valid_transforms_dog)

train_loader_dog = torch.utils.data.DataLoader(train_data_dog, batch_size=40, shuffle=True)
valid_loader_dog = torch.utils.data.DataLoader(valid_data_dog, batch_size=40)
test_loader_dog = torch.utils.data.DataLoader(test_data_dog, batch_size=40)

loaders_transfer={'train':train_loader_dog,
                  'valid': valid_loader_dog,
                  'test': test_loader_dog}

(IMPLEMENTATION) Model Architecture

Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable model_transfer.

In [23]:
import torchvision.models as models
import torch.nn as nn

## TODO: Specify model architecture 
model_transfer = models.resnet50(pretrained=True)
for params in model_transfer.parameters():
    params.require_grad = True
model_transfer.fc = nn.Linear(2048, 133)

if use_cuda:
    model_transfer = model_transfer.cuda()
Downloading: "https://download.pytorch.org/models/resnet50-19c8e357.pth" to /root/.torch/models/resnet50-19c8e357.pth
100%|██████████| 102502400/102502400 [00:01<00:00, 72648100.20it/s]
In [24]:
model_transfer
Out[24]:
ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (downsample): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (2): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
  )
  (layer2): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (2): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (3): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
  )
  (layer3): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (downsample): Sequential(
        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (2): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (3): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (4): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (5): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
  )
  (layer4): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
      (downsample): Sequential(
        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
    (2): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace)
    )
  )
  (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
  (fc): Linear(in_features=2048, out_features=133, bias=True)
)

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

  • Resnet50 is used because it is relatively fast to train
  • Resnet50 pretrained weights are used because it was trained on similar deta as the train set
  • In final classification layers only the last layer is changed to a dense linear with the same number of nodes as the number of classes of dogs which we are trying to predict. That is the part of the network which I am going to re-train so that it is able to classify the dog breeds.
  • This architecture is suitable beacuse the traing data is relative large and has similar data to what the original pretrained model was trained on.
  • The weights are Randomly initialized in the new fully connected layer and in the rest of the network, the weights are initialized using the pre-trained weights and re-train the entire neural network

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and the optimizer as optimizer_transfer below.

In [25]:
criterion_transfer = nn.CrossEntropyLoss()
optimizer_transfer = optim.SGD(model_transfer.parameters(), lr=0.01)

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.

In [26]:
# train the model
n_epochs = 15
model_transfer = train(n_epochs , loaders_transfer, model_transfer, optimizer_transfer, criterion_transfer, use_cuda, 'model_transfer.pt')

# load the model that got the best validation accuracy (uncomment the line below)
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
Epoch: 1 	Training Loss: 3.808256 	Validation Loss: 2.297208
........initiating save mode
Epoch: 2 	Training Loss: 1.895583 	Validation Loss: 1.200905
........initiating save mode
Epoch: 3 	Training Loss: 1.139708 	Validation Loss: 0.815288
........initiating save mode
Epoch: 4 	Training Loss: 0.816437 	Validation Loss: 0.647269
........initiating save mode
Epoch: 5 	Training Loss: 0.632947 	Validation Loss: 0.538353
........initiating save mode
Epoch: 6 	Training Loss: 0.511475 	Validation Loss: 0.471149
........initiating save mode
Epoch: 7 	Training Loss: 0.436376 	Validation Loss: 0.435332
........initiating save mode
Epoch: 8 	Training Loss: 0.365064 	Validation Loss: 0.418808
........initiating save mode
Epoch: 9 	Training Loss: 0.319853 	Validation Loss: 0.391794
........initiating save mode
Epoch: 10 	Training Loss: 0.279545 	Validation Loss: 0.391162
........initiating save mode
Epoch: 11 	Training Loss: 0.247332 	Validation Loss: 0.361073
........initiating save mode
Epoch: 12 	Training Loss: 0.224221 	Validation Loss: 0.354447
........initiating save mode
Epoch: 13 	Training Loss: 0.196717 	Validation Loss: 0.371353
Epoch: 14 	Training Loss: 0.170017 	Validation Loss: 0.333706
........initiating save mode
Epoch: 15 	Training Loss: 0.153742 	Validation Loss: 0.327975
........initiating save mode

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.

In [27]:
test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
Test Loss: 0.337666


Test Accuracy: 89% (748/836)

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan hound, etc) that is predicted by your model.

In [28]:
data_transfer = loaders_transfer
In [29]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
# list of class names by index, i.e. a name can be accessed like class_names[0]
from torch.autograd import Variable
class_names = [item[4:].replace("_", " ") for item in train_data_dog.classes]

def predict_breed_transfer(img_path):
    # load the image and return the predicted breeds for that image
    image = Image.open(img_path).convert('RGB')
    
    transformer = transforms.Compose([transforms.Resize(255),
                                       transforms.CenterCrop(224),
                                       transforms.ToTensor(),
                                       transforms.Normalize((0.5,),(0.5,))])
    
    img_processed = transformer(image).unsqueeze(0)
    
    if use_cuda:
        img_processed = img_processed.to('cuda')  
    img_var = Variable(img_processed, requires_grad= False)
    img_var = img_var.cuda()
    model_transfer.eval()
    output = model_transfer(img_var)
    _, pred = torch.max(output, 1)
    return class_names[pred]
    
prediction = predict_breed_transfer(dog_files[1002])
print(prediction)
Manchester terrier

Step 5: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and human_detector functions developed above. You are required to use your CNN from Step 4 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [30]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.

def run_app(img_path):
    ## handle cases for a human face, dog, and neither
    image = Image.open(img_path).convert('RGB')
    plt.imshow(image)
    plt.show()
    if dog_detector(img_path) == True:
        print('Dog found! Predicted breed is: ' + predict_breed_transfer(img_path))
    
    elif face_detector(img_path) == True:
        print("Human found! Most similar breed is: " + predict_breed_transfer(img_path))
    
    else:
        print('No human or dog was found')

Step 6: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer:

  • The breed detector could be improved by changing the classifier network to increase accuracy.
  • Some training dataset for some dog breed have very less images which can be added with more images to get better accuracy
  • Train the predictor to handle images with both human and dog
In [31]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.

## suggested code, below
for file in np.hstack((human_files[:3], dog_files[:3])):
    run_app(file)
Human found! Most similar breed is: Chihuahua
Human found! Most similar breed is: Neapolitan mastiff
Human found! Most similar breed is: American water spaniel
Dog found! Predicted breed is: Mastiff
Dog found! Predicted breed is: Mastiff
Dog found! Predicted breed is: Mastiff
In [37]:
my_human_files = ['./my_images/human3.jpg', './my_images/Accenture-Human-Machine-AI-James.png', './my_images/David_Windley.png' ]
my_dog_files = ['./my_images/American_water_spaniel_00648-Copy1.jpg', './my_images/Brittany_02625-Copy1.jpg', './my_images/Curly-coated_retriever_03896-Copy1.jpg','./my_images/Labrador_retriever_06449-Copy1.jpg','./my_images/Labrador_retriever_06455-Copy1.jpg','./my_images/Labrador_retriever_06457-Copy1.jpg','./my_images/Welsh_springer_spaniel_08203-Copy1.jpg']
In [38]:
for file in np.hstack((my_human_files, my_dog_files)):
    run_app(file)
Human found! Most similar breed is: Chinese shar-pei
Human found! Most similar breed is: Pointer
Human found! Most similar breed is: Dogue de bordeaux
Dog found! Predicted breed is: Curly-coated retriever
Dog found! Predicted breed is: Brittany
Dog found! Predicted breed is: Curly-coated retriever
Dog found! Predicted breed is: Labrador retriever
Dog found! Predicted breed is: Labrador retriever
Dog found! Predicted breed is: Labrador retriever
Dog found! Predicted breed is: Welsh springer spaniel
In [ ]: